Model predictive control strategy applied to different types of building for space heating

Abstract

In recent years, the concept of energy-efficient buildings has attracted widespread attention due to growing energy consumption in different types of buildings. The application of thermal energy storage (TES) systems, especially latent heat energy storage (LHES), has become a promising approach to improve thermal efficiency of buildings and hence reduce CO2 emissions. One way to achieve this, could be by implementing a model predictive control (MPC) strategy, using weather and electricity cost predictions. To this end, a heat exchanger unit containing a phase change material (PCM) as a LHES medium, thermally charged by solar energy was incorporated into three versions of a standard building. This paper reports on the use of EnergyPlus software to simulate the heating demand profile of these buildings, with Solving Constraint Integer Programs (SCIP) as the optimization tool. After applying MPC strategy, the energy costs of different building types were evaluated. Furthermore, the effect of prediction horizon and decision time step of MPC strategy, and PCM mass capacity on the performance of the MPC were all investigated in 1 and 7-day simulations. Results showed that by increasing the prediction horizon and PCM mass, more cost saving could be obtained. However, in terms of decision time step, although the study revealed that increasing it led to a higher energy saving, it made the system more sensitive to sharp changes as it failed to provide an accurate reading of the parameters and variables.

The study was partially funded by the Spanish Government (ENE2015-64117-C5-1-R (MINECO/FEDER) and ENE2015-64117-C5-3-R (MINECO/FEDER)). The authors at the University of Lleida would like to thank the Catalan Government for the quality accreditation given to their research group (2017 SGR 1537). GREiA is the certified agent for TECNIO in the category technology developers for the Government of Catalonia. The research leading to these results also received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement no. PIRSES-GA-2013-610692 (INNOSTORAGE).